Robot Teleoperation. Demonstrations have played a key role in robot learning for many decades [1]}, [2]}, [3]}, thus many approaches have been explored for collecting such demonstrations. While initially kinesthetic teaching was common [4]} in which a human operator directly moves the robot, more recently teleoperation has become the norm [5]}, [6]}, [7]}, [8]}, [9]}, since separating the human operator and the robot allows for more comfortable human control inputs and is crucial for training policies with image-based inputs. Research into teleoperation systems has focused on exploring different interfaces like VR headsets [5]}, [8]}, joysticks [12]} and smartphones [6]}. Yet, none of these works explores active assistance of the human operator during teleoperation. Others have investigated controlling high-DoF manipulators via low-DoF interfaces through learned embedding spaces [14]}, [15]} to allow people with disabilities to control robotic arms. In contrast, our approach trains assistive policies that automate part of the teleoperation process with the goal of enabling more scalable data collection.
